A Bayesian Approach to Uncertainty Modeling in OWL Ontology

Book Title: Proceedings of the International Conference on Advances in Intelligent Systems - Theory and Applications

Date: November 15, 2004

Abstract: Dealing with uncertainty is crucial in ontology
engineering tasks such as domain modeling, ontology reasoning,
and concept mapping between ontologies. This paper presents our
on-going research on modeling uncertainty in ontologies based on
Bayesian networks (BN). This includes 1) extending OWL to
allow additional probabilistic markups for attaching probability
information, 2) directly converting a probabilistically annotated
OWL ontology into a BN structure by a set of structural
translation rules, and 3) constructing the conditional probability
tables (CPTs) of this BN using a new method based on iterative
proportiobal fitting procedure (IPFP). The translated BN can
support more accurate ontology reasoning under uncertainty as
Bayesian inferences.